Facilitating Operation of a Machine Learning Environment

• US 20140310208A1
• Filed: 04/10/2013
• Published: 10/16/2014
• Est. Priority Date: 04/10/2013
• Status: Abandoned Application

• Current Assignee: Emotient, Inc. (Apple Inc.)
• Original Assignee: Machine Perception Technologies Inc. (Apple Inc.)
• Inventors: Movellan, Javier R., Fasel, Ian, Polizo, James, Whitehill, Jacob, Susskind, Joshua M.
• Application Number:
  US13/860,467
• Publication Number:
  US 20140310208A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06V 10/426 Graphical representations
• CPC Class Codes: G06V 10/449 Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
• CPC Class Codes: G06V 10/464 using a plurality of salient features, e.g. bag-of-words [BoW] representations
• CPC Class Codes: G06V 40/171 Local features and components; Facial parts eye characteristics G06V40/18; Occluding parts, e.g. glasses; Geometrical relationships
• CPC Class Codes: G06V 40/175 Static expression

Abstract

Machine learning systems are represented as directed acyclic graphs, where the nodes represent functional modules in the system and edges represent input/output relations between the functional modules. A machine learning environment can then be created to facilitate the training and operation of these machine learning systems.

Claims

1. A computer-implemented method for facilitating operation of a machine learning environment, the environment comprising functional modules that can be configured and linked in different ways to define different machine learning instances, the method comprising:

receiving a directed acyclic graph defining a machine learning instance, the directed acyclic graph containing nodes and edges connecting the nodes, the nodes identifying functional modules, the edges entering a node representing inputs to the functional module and the edges exiting a node representing outputs of the functional module; and

executing the machine learning instance defined by the acyclic graph.

2. The method of claim 1 further comprising:

saving a final output of the machine learning instance.

3. The method of claim 1 further comprising:

saving an interim output of the machine learning instance.

4. The method of claim 1 wherein the step of executing the machine learning instance comprises:

identifying that an output of a component of the machine learning instance has been previously saved; and

retrieving the saved output rather than re-executing the component.

5. The method of claim 1 wherein the step of executing the machine learning instance comprises:

linking output of one functional module in the machine learning instance to input of a next functional module of the machine learning instance at run-time.

6. The method of claim 1 wherein the functional modules communicate through a shared file system.

7. The method of claim 1 wherein the nodes identify functional modules and at least one attribute for at least one functional module.

8. The method of claim 7 wherein the at least one attribute is a version number for a software code for the functional module.

9. The method of claim 7 wherein the functional module contains numerical, categorical, or structural parameters determining by supervised learning, and the at least one attribute identifies values for the numerical parameters.

10. The method of claim 1 wherein at least one functional module is a sensor module that provides initial data as input to other functional modules for processing.

11. The method of claim 1 wherein at least one functional module is a teacher module that receives input data and provides corresponding training outputs, the input data and corresponding training outputs forming a training set for training a parameterized model implemented by other functional modules.

12. The method of claim 1 wherein at least one functional module is a learning module that receives a training set as input and undergoes learning of a parameterized model based on the training set.

13. The method of claim 12 wherein the learning module outputs numerical, categorical, or structural parameters determined by learning for a parameterized model.

14. The method of claim 1 wherein at least one functional module is a perceiver module that receives data as input and applies a parameterized model to produce corresponding outputs.

15. The method of claim 14 wherein the perceiver module further receives numerical parameters for the parameterized model as input.

16. The method of claim 15 wherein at least one functional module is a tester module that receives inputs from the perceiver model and evaluates an accuracy of the perceiver module.

17. The method of claim 1 wherein the machine learning environment contains sufficient functional modules to define a machine learning instance that implements emotion detection from facial images.

18. The method of claim 17 wherein at least one of the modules is a face detection module that identifies face location within facial images.

19. The method of claim 17 wherein at least one of the modules is a facial landmark detection module that identifies locations of facial landmarks within an identified face.

20. The method of claim 17 wherein at least one of the modules is an emotion detection module that outputs an indication of emotion based on identified facial landmarks within a face.

21. The method of claim 1 wherein the machine learning environment contains sufficient functional modules to define a machine learning instance that implements smile detection from facial images.

22. The method of claim 21 wherein at least one of the modules is a smile detection module that outputs an estimate of whether a smile is present based on identified facial landmarks within a facial image.

23. The method of claim 1 wherein the step of receiving the directed acyclic graph comprises receiving a text string representing the directed acyclic graph.

24. The method of claim 1 wherein the step of receiving the directed acyclic graph comprises receiving a graphical representation of the directed acyclic graph.

25. A tangible computer readable medium containing instructions that, when executed by a processor, execute a method for facilitating operation of a machine learning environment, the environment comprising functional modules that can be configured and linked in different ways to define different machine learning instances, the method comprising:

receiving a directed acyclic graph defining a machine learning instance, the directed acyclic graph containing nodes and edges connecting the nodes, the nodes identifying functional modules, the edges entering a node representing inputs to the functional module and the edges exiting a node representing outputs of the functional module; and

executing the machine learning instance defined by the acyclic graph.

26. A tool for facilitating operation of a machine learning environment, the environment comprising functional modules that can be configured and linked in different ways to define different machine learning instances, the method comprising:

means for receiving a directed acyclic graph defining a machine learning instance, the directed acyclic graph containing nodes and edges connecting the nodes, the nodes identifying functional modules, the edges entering a node representing inputs to the functional module and the edges exiting a node representing outputs of the functional module; and

means for executing the machine learning instance defined by the acyclic graph.